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12 logging best practices for daily development: a comprehensive tutorial

logging is crucial for debugging, monitoring, and auditing applications. effective logging can save you countless hours of troubleshooting and provide valuable insights into your software's behavior. this tutorial outlines 12 best practices, accompanied by code examples (primarily in python, but the concepts apply across languages), to help you implement robust and informative logging in your daily development workflow.

*1. use a structured logging format:*

*do:* instead of simple strings, use a structured logging format (e.g., json) to facilitate easier parsing and analysis by log management tools. this allows for efficient searching, filtering, and aggregation of log data.

*don't:* rely on unstructured, free-form log messages. they are difficult to parse automatically.



*2. log at appropriate levels:*

*do:* utilize different log levels (debug, info, warning, error, critical) to categorize messages based on their severity. this allows for granular control over which messages are displayed or stored.

*don't:* overuse the error level for non-critical issues. reserve error for situations that hinder the application's functionality.




*3. include contextual information:*

*do:* include relevant context in your log messages, such as timestamps, user ids, request ids, and relevant variables. this helps to trace the flow of events and identify the root cause of problems.

*don't:* log only generic messages without context. this makes debugging significantly harder.



*4. use a logging framework:*

*do:* use a dedicated logging framework (like python's `logging` module, log4j, logback) instead of printing directly to the console or writing to files manually. frameworks provide features like log rotation, filtering, and different output handlers.

*don't:* rely on `print()` statements for logging in production code. they are not flexible ...

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